Abstract
High-dose ionizing radiation is associated with circulatory disease. Risks from lower-dose fractionated exposures, such as from diagnostic radiation procedures, remain unclear. In this study we aimed to ascertain the relationship between fractionated low-to-medium dose radiation exposure and circulatory disease mortality in a cohort of 13,568 tuberculosis patients in Massachusetts, some with fluoroscopy screenings, between 1916 and 1961 and follow-up until the end of 2002. Analysis of mortality was in relation to cumulative thyroid (cerebrovascular) or lung (all other circulatory disease) radiation dose via Poisson regression. Over the full dose range, there was no overall radiation-related excess risk of death from circulatory disease (n = 3221; excess relative risk/Gy −0.023; 95 % CI −0.067, 0.028; p = 0.3574). Risk was somewhat elevated in hypertensive heart disease (n = 89; excess relative risk/Gy 0.357; 95 % CI −0.043, 1.030, p = 0.0907) and slightly decreased in ischemic heart disease (n = 1950; excess relative risk/Gy −0.077; 95 % CI −0.130, −0.012; p = 0.0211). However, under 0.5 Gy, there was a borderline significant increasing trend for all circulatory disease (excess relative risk/Gy 0.345; 95 % CI −0.032, 0.764; p = 0.0743) and for ischemic heart disease (excess relative risk/Gy 0.465; 95 % CI, −0.032, 1.034, p = 0.0682). Pneumolobectomy increased radiation–associated risk (excess relative risk/Gy 0.252; 95 % CI 0.024, 0.579). Fractionation of dose did not modify excess risk. In summary, we found no evidence of radiation-associated excess circulatory death risk overall, but there are indications of excess circulatory death risk at lower doses (<0.5 Gy). Although consistent with other radiation-exposed groups, the indications of higher risk at lower doses are unusual and should be confirmed against other data.
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Acknowledgments
MPL and AVB were supported by the Intramural Research Program of the National Institutes of Health, the National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics, and LBZ by NCI grants 5K07CA132918 and 1R03CA188614. The authors are grateful for the detailed and helpful comments of Dr Marvin Rosenstein and a referee.
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Appendices
Appendix 1: Background models selected
See Tables 8, 9, 10, 11, 12, 13 and 14.
Appendix 2: Effect of alternative background models selected via minimizing Akaike information criterion
In this appendix we consider an alternative set of explanatory background models for each endpoint, selected via an automatic variable selection process, by minimizing the Akaike information criterion (AIC) [24, 25]. Minimizing AIC is a standard method of variable selection that avoids over-parameterised (and therefore over-fitted) models. AIC penalizes against overfitting by adding 2 × [number of fitted parameters] to the model deviance. We used an iterative mixed-forward–backward stepwise procedure to minimize AIC using models with Poisson error via R [26].
The models used the set of candidate variables listed in Table 15, in which the optimal models chosen are also indicated. We provide the analog of Table 2 using these alternative background models in Table 16.
Appendix 3: Generalized additive models (GAM)
See Table 17.
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Little, M.P., Zablotska, L.B., Brenner, A.V. et al. Circulatory disease mortality in the Massachusetts tuberculosis fluoroscopy cohort study. Eur J Epidemiol 31, 287–309 (2016). https://doi.org/10.1007/s10654-015-0075-9
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DOI: https://doi.org/10.1007/s10654-015-0075-9